Search Results for author:
Found 11 papers, 3 papers with code
Mixed-Output Gaussian Process Latent Variable Models
Our framework allows the use of a range of priors for the weights of each observation.
Delayed Feedback in Generalised Linear Bandits Revisited
However, the stringent requirement for immediate rewards is unmet in many real-world applications where the reward is almost always delayed.
Variational Bayes for high-dimensional proportional hazards models with applications within gene expression
Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification.
Optimism and Delays in Episodic Reinforcement Learning
In this paper, we study the impact of delayed feedback in episodic reinforcement learning from a theoretical perspective and propose two general-purpose approaches to handling the delays.
BART-based inference for Poisson processes
The effectiveness of Bayesian Additive Regression Trees (BART) has been demonstrated in a variety of contexts including non-parametric regression and classification.
A Bayesian nonparametric test for conditional independence
This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a dataset in favour of the dependence or independence of two variables conditional on a third.
Interpreting Deep Neural Networks Through Variable Importance
While the success of deep neural networks (DNNs) is well-established across a variety of domains, our ability to explain and interpret these methods is limited.
Large-Scale Kernel Methods for Independence Testing
Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible framework for fully nonparametric hypothesis tests of independence, which can capture any type of departure from independence, including nonlinear associations and multivariate interactions.
Bayesian Learning of Kernel Embeddings
The posterior mean of our model is closely related to recently proposed shrinkage estimators for kernel mean embeddings, while the posterior uncertainty is a new, interesting feature with various possible applications.
On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo
Here we discuss how to construct the perturbation kernels that are required in ABC SMC approaches, in order to construct a set of distributions that start out from a suitably defined prior and converge towards the unknown posterior.
Computation
Parametric Bandits: The Generalized Linear Case
We consider structured multi-armed bandit tasks in which the agent is guided by prior structural knowledge that can be exploited to efficiently select the optimal arm(s) in situations where the number of arms is large, or even infinite.
